Pattern Analysis

As part of the assignment, I was asked to complete 4 exercises the updated version of Chapter 8: Analyzing Patterns from GIS Tutorial II - Spatial Analysis Workbook by David W. Allen (2016) provided in the theme Assignment block. The leaning objectives for this assignment were to demonstrate an elementary understanding of spatial pattern analysis concepts and demonstrate understanding of a range of methods for analyzing global spatial patterns.

Strategies: For this problem, I used ESRI's ArcGIS Pro 2.8 along with tools such as Average nearest neighbor, GetisOrd General G, Multidistance clustering, or Ripley's K function and spatial autocorrelation, or global Moran's I For this project, the instructor provided data for the assignment in a folder containing 4 mxd. Files MXD (Map Exchange Document) which is a file format in which the maps created from ArcGIS software can be stored. MXD not only stores maps but also stores the symbology, layout, hyperlinks, toolbars added.

Method

Exercise 1: To complete this exercise I performed to calculates a nearest neighbor index based on the average distance from each feature to its nearest neighboring feature. To interpret the results, I looked at the nearest neighbor ratio, or index, and consider what results the other datasets might produce.

Exercise 2: To complete this assignment I used the minimum, the maximum, and the average distance to the specified Nth nearest neighbor (N is an input parameter) for a set of features. Results are written as tool execution messages that I interpreted individually. Follow with performing Calculate Distance Band from Neighbor Count (Spatial Statistics) five times using distances ranging from 700 feet to 1200 feet which Returns the minimum, the maximum, and the average distance to the specified Nth nearest neighbor (N is an input parameter) for a set of features in this case false alarms The results are written as tool execution messages that I interpreted individually and recorded.

Exercise 3: To complete this exercise I used the Multi-Distance Spatial Cluster Analysis (Ripley's K Function) (Spatial Statistics) which Determines whether features, or the values associated with features, exhibit statistically significant clustering or dispersion over a range of distances using Beginning Distance of 200 and Distance Increment of 100. Followed with performing calculate field to find the difference between Observed K and Hi confidence interval then from this calculation I created a graph.

Exercise 4: to complete this exercise I used the aggregate point tool to convert the patron location point feature into a polygon feature then I used the spatial join tool to Joins attributes from the newly create polygon feature to the grid. Next, I performed the Spatial Autocorrelation (Global Moran's I) which Measures spatial autocorrelation based on feature locations and attribute values using the Global Moran's I statistic 6 times using Test distances between 2800 ft - 3800 ft with 200 ft increments.

Process diagram

During the completion of this assisment I learn about how Ripley's K function can be used to examine the clustering of a certain plant species. Additionally, regional search may be done for the significance of clustering rather than using only the next nearest neighbor using this tool.

New Problem Description Ripley's K function can be used to examine the clustering of longleaf pines species present in a site where gopher tortoise resides.

Data Needed Shapefile of nature preserve from NC OneMap. Data on longleaf pines from Forest manager including GPS locations of trees and another table with gopher burrows count(all data containing field tract.).

Analysis Procedures Use calculate field to match the field type of all my tables then add a join between gopher tortoise and longleaf pines. Followed by performing a Multi-Distance Spatial Cluster Analysis (Ripley's K Function) (Spatial Statistics) to exhibit statistically significant clustering of longleaf pines at 10 feet. And Distance Increment of 2 feet. Followed with performing calculate field to find the difference between Observed K and Hi confidence interval then from this calculation I will create graphs to show relationship.